> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nrai.io/llms.txt
> Use this file to discover all available pages before exploring further.

# System Requirements & Performance

> Optimize your NRAI deployment with intelligent resource planning and performance insights

<div className="nrai-performance-hero">
  <h1>🚀 Power Your NRAI Instance</h1>
  <p>Discover the optimal hardware configuration for your intelligent automation needs. NRAI's adaptive architecture scales seamlessly from development to enterprise-grade deployments.</p>
</div>

## 🎯 Smart Resource Planning

NRAI's revolutionary architecture prioritizes **memory efficiency** over raw CPU power, utilizing intelligent caching and predictive resource allocation to maximize performance per dollar spent.

<Tabs>
  <Tab title="🏠 Development Setup">
    Perfect for testing, prototyping, and small-scale automation projects.

    ### Minimum Configuration

    | Service        | Memory | CPU      | Storage   | Purpose                 |
    | -------------- | ------ | -------- | --------- | ----------------------- |
    | **NRAI Core**  | 2 GB   | 1 vCPU   | 20 GB SSD | Main application engine |
    | **PostgreSQL** | 512 MB | 0.5 vCPU | 10 GB SSD | Data persistence        |
    | **Redis**      | 256 MB | 0.5 vCPU | 2 GB SSD  | Job queue & caching     |

    <Note>
      **💡 Pro Tip:** This configuration handles up to **100,000 workflow executions** per month with excellent response times.
    </Note>
  </Tab>

  <Tab title="🏢 Production Ready">
    Designed for business-critical workflows with high availability requirements.

    ### Recommended Configuration

    | Service        | Memory | CPU    | Storage    | Purpose                        |
    | -------------- | ------ | ------ | ---------- | ------------------------------ |
    | **NRAI Core**  | 8 GB   | 2 vCPU | 100 GB SSD | Enhanced processing power      |
    | **PostgreSQL** | 4 GB   | 2 vCPU | 200 GB SSD | Optimized database performance |
    | **Redis**      | 2 GB   | 1 vCPU | 20 GB SSD  | High-speed job processing      |

    <Note>
      **🎯 Performance Target:** Handles **5+ million executions** monthly with sub-second response times.
    </Note>
  </Tab>

  <Tab title="🌐 Enterprise Scale">
    Maximum performance for large organizations with complex automation needs.

    ### High-Performance Configuration

    | Service        | Memory | CPU    | Storage     | Purpose              |
    | -------------- | ------ | ------ | ----------- | -------------------- |
    | **NRAI Core**  | 16 GB  | 4 vCPU | 500 GB NVMe | Maximum throughput   |
    | **PostgreSQL** | 8 GB   | 4 vCPU | 1 TB NVMe   | Enterprise database  |
    | **Redis**      | 4 GB   | 2 vCPU | 100 GB NVMe | Ultra-fast job queue |

    <Note>
      **⚡ Enterprise Power:** Processes **50+ million executions** monthly with guaranteed 99.9% uptime.
    </Note>
  </Tab>
</Tabs>

## 🧠 Intelligent Scaling Strategies

### Dynamic Resource Allocation

NRAI's AI-powered resource management automatically adjusts to your workload patterns:

<Steps>
  <Step title="🔍 Pattern Recognition">
    The system learns your workflow patterns and predicts resource needs before bottlenecks occur
  </Step>

  <Step title="⚡ Auto-Scaling">
    Resources scale up during peak times and down during quiet periods, optimizing costs
  </Step>

  <Step title="🎯 Load Balancing">
    Intelligent distribution of workloads across available resources for maximum efficiency
  </Step>

  <Step title="📊 Performance Optimization">
    Continuous monitoring and adjustment based on real-time performance metrics
  </Step>
</Steps>

### Component-Specific Scaling

<Accordion>
  <AccordionItem title="🗄️ PostgreSQL Optimization">
    **Memory-First Approach:**

    * Increase `shared_buffers` to 25% of total RAM
    * Set `effective_cache_size` to 75% of total RAM
    * Configure `work_mem` based on concurrent connections

    **Storage Optimization:**

    * Use NVMe SSDs for maximum IOPS performance
    * Enable connection pooling with PgBouncer
    * Consider read replicas for high-read workloads

    <Tip>
      **💾 File Storage Tip:** Offload file storage to S3-compatible services to reduce database load by up to 70%.
    </Tip>
  </AccordionItem>

  <AccordionItem title="⚡ Redis Performance Tuning">
    **Memory Configuration:**

    * Set `maxmemory` to 80% of available RAM
    * Use `allkeys-lru` eviction policy for optimal performance
    * Enable persistence only if data durability is critical

    **Network Optimization:**

    * Use Redis Cluster for horizontal scaling
    * Enable compression for large payloads
    * Configure appropriate timeout values

    <Warning>
      **⚠️ Important:** Redis is primarily used for job queuing. Memory requirements scale with job complexity, not volume.
    </Warning>
  </AccordionItem>

  <AccordionItem title="🚀 NRAI Core Scaling">
    **Horizontal Scaling:**

    * NRAI containers are completely stateless
    * Add instances behind a load balancer for increased capacity
    * Each instance can handle independent workloads

    **Concurrency Configuration:**

    ```bash theme={null}
    # Workflow execution concurrency
    FLOW_WORKER_CONCURRENCY=50

    # Scheduled job concurrency  
    SCHEDULED_WORKER_CONCURRENCY=25

    # API request handling
    WEB_WORKER_CONCURRENCY=100
    ```

    <Tip>
      **🎛️ Tuning Tip:** Start with conservative values and increase based on monitoring data. Each workflow type has different resource requirements.
    </Tip>
  </AccordionItem>
</Accordion>

## 📈 Performance Benchmarks

### Real-World Performance Data

<CardGroup cols={2}>
  <Card title="🏃‍♂️ Execution Speed" color="#10b981">
    **Average Response Times:**

    * Simple workflows: \< 50ms
    * Complex integrations: \< 500ms
    * AI-powered workflows: \< 2s
    * Batch processing: 1000+ items/minute
  </Card>

  <Card title="📊 Throughput Capacity" color="#6366f1">
    **Processing Volumes:**

    * Development: 100K+ executions/month
    * Production: 5M+ executions/month
    * Enterprise: 50M+ executions/month
    * Peak burst: 10,000 concurrent jobs
  </Card>
</CardGroup>

### Scaling Milestones

| Monthly Executions | Recommended Setup | Expected Response Time | Cost Efficiency |
| ------------------ | ----------------- | ---------------------- | --------------- |
| 0 - 100K           | Development       | \< 100ms               | ⭐⭐⭐⭐⭐           |
| 100K - 1M          | Production        | \< 200ms               | ⭐⭐⭐⭐            |
| 1M - 10M           | Production+       | \< 300ms               | ⭐⭐⭐             |
| 10M+               | Enterprise        | \< 500ms               | ⭐⭐              |

## 🔧 Advanced Configuration

### Environment Variables for Performance

```bash theme={null}
# Core Performance Settings
NRAI_MAX_MEMORY_USAGE=8192  # MB
NRAI_CPU_OPTIMIZATION=true
NRAI_CACHE_STRATEGY=intelligent

# Database Optimization
DB_CONNECTION_POOL_SIZE=20
DB_QUERY_TIMEOUT=30000
DB_STATEMENT_TIMEOUT=60000

# Redis Configuration
REDIS_MAX_CONNECTIONS=100
REDIS_COMMAND_TIMEOUT=5000
REDIS_RETRY_ATTEMPTS=3

# Monitoring & Observability
ENABLE_PERFORMANCE_METRICS=true
METRICS_COLLECTION_INTERVAL=60
LOG_PERFORMANCE_WARNINGS=true
```

### Cloud Provider Recommendations

<Tabs>
  <Tab title="☁️ AWS">
    **Recommended Instance Types:**

    * Development: `t3.medium` (2 vCPU, 4 GB RAM)
    * Production: `m5.large` (2 vCPU, 8 GB RAM)
    * Enterprise: `m5.xlarge` (4 vCPU, 16 GB RAM)

    **Storage Options:**

    * Use `gp3` volumes for cost-effective performance
    * Consider `io2` for high-IOPS requirements
    * Enable EBS optimization for better throughput
  </Tab>

  <Tab title="🌐 Google Cloud">
    **Recommended Machine Types:**

    * Development: `e2-standard-2` (2 vCPU, 8 GB RAM)
    * Production: `n2-standard-4` (4 vCPU, 16 GB RAM)
    * Enterprise: `n2-standard-8` (8 vCPU, 32 GB RAM)

    **Storage Options:**

    * Use SSD persistent disks for optimal performance
    * Enable automatic disk resizing
    * Consider regional persistent disks for HA
  </Tab>

  <Tab title="🔷 Azure">
    **Recommended VM Sizes:**

    * Development: `Standard_B2s` (2 vCPU, 4 GB RAM)
    * Production: `Standard_D4s_v3` (4 vCPU, 16 GB RAM)
    * Enterprise: `Standard_D8s_v3` (8 vCPU, 32 GB RAM)

    **Storage Options:**

    * Use Premium SSD for production workloads
    * Enable accelerated networking
    * Consider availability zones for redundancy
  </Tab>
</Tabs>

## 🎯 Performance Monitoring

### Key Metrics to Track

<CardGroup cols={3}>
  <Card title="🚀 Response Time" color="#f59e0b">
    Monitor average and 95th percentile response times for all workflow types
  </Card>

  <Card title="💾 Memory Usage" color="#ef4444">
    Track memory consumption patterns and identify potential leaks
  </Card>

  <Card title="🔄 Queue Depth" color="#8b5cf6">
    Monitor job queue sizes to prevent bottlenecks
  </Card>
</CardGroup>

<Note>
  **📊 Pro Monitoring Tip:** Set up alerts for response times > 1s, memory usage > 80%, and queue depth > 1000 jobs.
</Note>

***

<div className="nrai-performance-footer">
  <h3>🎯 Ready to Optimize Your NRAI Deployment?</h3>
  <p>Our performance engineering team is here to help you achieve maximum efficiency. Contact us for personalized optimization recommendations.</p>

  <div className="nrai-footer-buttons">
    <a href="/install/architecture/overview" className="nrai-button">📚 Architecture Guide</a>
    <a href="/install/configuration/environment-variables" className="nrai-button">⚙️ Configuration</a>
    <a href="https://nrai.io/support" className="nrai-button">🤝 Get Support</a>
  </div>
</div>

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